library(dplyr)        # Data manipulation
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(reshape2)     # Data reshaping for ggplot
library(ggplot2)      # Data visualization    
library(readxl)        # CSV file I/O, e.g. the read_csv function
library(zipcode)
library(maps)
library(ggmap)
library(shiny)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggmap':
## 
##     wind
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
Speed_dating = read.csv("SpeedDating.csv", na.strings = c("",".","NA"))
variables = c("iid","gender", "age","income", "field","attr1_1", "sinc1_1", "intel1_1", "fun1_1", "amb1_1", "shar1_1")
Speed_dating_edit = Speed_dating[variables]
Speed_dating_combined = Speed_dating_edit[!duplicated(Speed_dating_edit$iid), ]
Speed_dating_clean = na.omit(Speed_dating_combined)

Find the average of the 5 variables

Speed_dating_average = data.frame(name = c("Attraction", "Sincerity", "Intelligence ", "Fun","Ambition","Share"), mean= c( mean(Speed_dating_clean$attr1_1), mean(Speed_dating_clean$sinc1_1), mean(Speed_dating_clean$intel1_1), mean(Speed_dating_clean$fun1_1), mean(Speed_dating_clean$amb1_1), mean(Speed_dating_clean$shar1_1)))

ggplot(Speed_dating_average, aes(x= name, y = mean)) + geom_bar(stat = "identity") + xlab("") + ylab("Mean Score")

Find the percentage of matched and divide into two groups (high and low)

library(data.table)
## 
## Attaching package: 'data.table'
## The following objects are masked from 'package:reshape2':
## 
##     dcast, melt
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
library(dplyr)
Speed_dating_count = tally(group_by(Speed_dating, iid))
Speed_dating_sum = aggregate(match~ iid, data = Speed_dating, sum)
Speed_dating_table = merge(Speed_dating_sum, Speed_dating_count, by = "iid")
Speed_dating_table$percent = (Speed_dating_table$match / Speed_dating_table$n) * 100
Speed_dating_table$group = ifelse(Speed_dating_table$percent < 50, "Low Match","High Match")
Speed_dating_final = merge(Speed_dating_table, Speed_dating_clean, by = "iid")
Speed_dating_final
##     iid match  n   percent      group gender age     income
## 1     1     4 10 40.000000  Low Match      0  21  69,487.00
## 2     2     2 10 20.000000  Low Match      0  24  65,929.00
## 3     4     2 10 20.000000  Low Match      0  23  37,754.00
## 4     5     2 10 20.000000  Low Match      0  21  86,340.00
## 5     6     2 10 20.000000  Low Match      0  23  60,304.00
## 6     7     2 10 20.000000  Low Match      0  22  54,620.00
## 7    10     2 10 20.000000  Low Match      0  26  48,652.00
## 8    12     2 10 20.000000  Low Match      1  22  29,237.00
## 9    14     8 10 80.000000 High Match      1  23  56,580.00
## 10   15     3 10 30.000000  Low Match      1  24  36,782.00
## 11   17     1 10 10.000000  Low Match      1  30  38,548.00
## 12   18     1 10 10.000000  Low Match      1  27  52,010.00
## 13   22     2 16 12.500000  Low Match      0  27  28,418.00
## 14   24     0 16  0.000000  Low Match      0  27  43,185.00
## 15   25     0 16  0.000000  Low Match      0  25  23,152.00
## 16   26     0 16  0.000000  Low Match      0  23  43,664.00
## 17   27     2 16 12.500000  Low Match      0  22  48,441.00
## 18   29     2 16 12.500000  Low Match      0  22  61,152.00
## 19   31     4 16 25.000000  Low Match      0  24  36,485.00
## 20   33     0 16  0.000000  Low Match      0  30  41,507.00
## 21   35     5 16 31.250000  Low Match      0  25  17,134.00
## 22   37     2 16 12.500000  Low Match      0  27  30,038.00
## 23   38     3 16 18.750000  Low Match      0  23  33,772.00
## 24   39     1 16  6.250000  Low Match      0  24  24,997.00
## 25   40     0 19  0.000000  Low Match      1  25  42,096.00
## 26   42     0 19  0.000000  Low Match      1  23  28,891.00
## 27   49     6 19 31.578947  Low Match      1  25  62,635.00
## 28   50     4 19 21.052632  Low Match      1  27  12,063.00
## 29   51     1 19  5.263158  Low Match      1  23  29,809.00
## 30   52     1 19  5.263158  Low Match      1  21  26,482.00
## 31   54     0 19  0.000000  Low Match      1  28  30,147.00
## 32   55     3 19 15.789474  Low Match      1  23  39,919.00
## 33   56     2 10 20.000000  Low Match      0  23  41,466.00
## 34   61     2 10 20.000000  Low Match      0  28  23,988.00
## 35   62     3 10 30.000000  Low Match      0  26  28,989.00
## 36   63     1 10 10.000000  Low Match      0  24  50,948.00
## 37   64     1 10 10.000000  Low Match      0  27  38,022.00
## 38   67     2 10 20.000000  Low Match      1  22  47,559.00
## 39   68     0 10  0.000000  Low Match      1  27  53,539.00
## 40   70     1 10 10.000000  Low Match      1  26  32,159.00
## 41   71     3 10 30.000000  Low Match      1  21  53,940.00
## 42   75     2 10 20.000000  Low Match      1  24  40,753.00
## 43   76     5 18 27.777778  Low Match      0  34  38,207.00
## 44   77     6 18 33.333333  Low Match      0  22  46,166.00
## 45   78     3 18 16.666667  Low Match      0  23  30,973.00
## 46   81     3 18 16.666667  Low Match      0  28  28,317.00
## 47   82     5 18 27.777778  Low Match      0  25  26,645.00
## 48   85     1 18  5.555556  Low Match      0  30  25,589.00
## 49   86     8 18 44.444444  Low Match      0  22  55,223.00
## 50   88     0 18  0.000000  Low Match      0  23 109,031.00
## 51   91     8 18 44.444444  Low Match      0  25  40,409.00
## 52   92     7 18 38.888889  Low Match      0  29  21,597.00
## 53   95     1 18  5.555556  Low Match      1  26  76,624.00
## 54   97     5 18 27.777778  Low Match      1  29  35,968.00
## 55   98     2 18 11.111111  Low Match      1  39  51,725.00
## 56   99     8 18 44.444444  Low Match      1  28  55,419.00
## 57  103     1 18  5.555556  Low Match      1  26  55,550.00
## 58  104     5 18 27.777778  Low Match      1  27  26,682.00
## 59  105     8 18 44.444444  Low Match      1  25  41,547.00
## 60  106     2 18 11.111111  Low Match      1  22  23,361.00
## 61  107    11 18 61.111111 High Match      1  21  74,893.00
## 62  110     4 18 22.222222  Low Match      1  28  52,804.00
## 63  113     3 10 30.000000  Low Match      0  21  53,923.00
## 64  115     3 10 30.000000  Low Match      0  21  27,094.00
## 65  117     1 10 10.000000  Low Match      0  21  57,213.00
## 66  120     4 10 40.000000  Low Match      0  20  42,390.00
## 67  121     0 10  0.000000  Low Match      0  21  43,636.00
## 68  122     3 10 30.000000  Low Match      1  22  57,887.00
## 69  123     0 10  0.000000  Low Match      1  18  30,768.00
## 70  124     0 10  0.000000  Low Match      1  22  66,699.00
## 71  125     7 10 70.000000 High Match      1  21  45,360.00
## 72  126     2 10 20.000000  Low Match      1  22  55,080.00
## 73  131     0 10  0.000000  Low Match      1  19  17,378.00
## 74  132     1  5 20.000000  Low Match      0  27  40,375.00
## 75  134     1  5 20.000000  Low Match      0  23  48,929.00
## 76  137     1  5 20.000000  Low Match      1  26  78,193.00
## 77  138     1  5 20.000000  Low Match      1  32  63,351.00
## 78  142     7 16 43.750000  Low Match      0  24  50,745.00
## 79  144     1 16  6.250000  Low Match      0  23  29,279.00
## 80  145     0 16  0.000000  Low Match      0  24  38,774.00
## 81  146     1 16  6.250000  Low Match      0  36  58,802.00
## 82  147     5 16 31.250000  Low Match      0  26  41,831.00
## 83  149     5 16 31.250000  Low Match      0  28  52,186.00
## 84  150     2 16 12.500000  Low Match      0  31  55,080.00
## 85  154     7 16 43.750000  Low Match      0  27  97,857.00
## 86  155     1 16  6.250000  Low Match      0  27  74,624.00
## 87  156     4 16 25.000000  Low Match      0  30  21,590.00
## 88  157     1 16  6.250000  Low Match      0  28  38,832.00
## 89  158     0 16  0.000000  Low Match      1  23  37,248.00
## 90  160     3 16 18.750000  Low Match      1  29  28,240.00
## 91  161     2 16 12.500000  Low Match      1  22  53,771.00
## 92  163     3 16 18.750000  Low Match      1  26  56,096.00
## 93  164     2 16 12.500000  Low Match      1  27  31,560.00
## 94  165     3 16 18.750000  Low Match      1  30  52,467.00
## 95  166     3 16 18.750000  Low Match      1  26  80,006.00
## 96  167     3 16 18.750000  Low Match      1  23  47,572.00
## 97  168     1 16  6.250000  Low Match      1  23  22,439.00
## 98  169     5 16 31.250000  Low Match      1  28  31,383.00
## 99  174     2 10 20.000000  Low Match      0  29  58,802.00
## 100 175     3 10 30.000000  Low Match      0  23  40,749.00
## 101 177     0 10  0.000000  Low Match      0  22  47,997.00
## 102 179     1 10 10.000000  Low Match      0  30  78,704.00
## 103 181     5 10 50.000000 High Match      0  22  31,143.00
## 104 184     4 10 40.000000  Low Match      1  34  32,129.00
## 105 186     3 10 30.000000  Low Match      1  24  44,195.00
## 106 188     1 10 10.000000  Low Match      1  21  46,837.00
## 107 192     1 10 10.000000  Low Match      1  24  97,972.00
## 108 193     2 10 20.000000  Low Match      1  25  35,960.00
## 109 194     1 20  5.000000  Low Match      0  24  65,708.00
## 110 195     5 20 25.000000  Low Match      0  27  49,466.00
## 111 197     3 20 15.000000  Low Match      0  24  53,229.00
## 112 199     5 20 25.000000  Low Match      0  30  32,649.00
## 113 200     3 20 15.000000  Low Match      0  25  35,867.00
## 114 202     2 20 10.000000  Low Match      0  22  40,244.00
## 115 203     0 20  0.000000  Low Match      0  22  42,640.00
## 116 204     0 20  0.000000  Low Match      0  26  52,388.00
## 117 208    11 20 55.000000 High Match      0  25  62,875.00
## 118 209     0 20  0.000000  Low Match      0  28  30,855.00
## 119 210     3 20 15.000000  Low Match      0  29  46,800.00
## 120 212     9 20 45.000000  Low Match      0  27  45,695.00
## 121 214     2 20 10.000000  Low Match      1  23  46,792.00
## 122 215     5 20 25.000000  Low Match      1  26  53,501.00
## 123 216     0 20  0.000000  Low Match      1  27  55,080.00
## 124 217     8 20 40.000000  Low Match      1  26  64,716.00
## 125 223     1 20  5.000000  Low Match      1  28  27,248.00
## 126 226     4 20 20.000000  Low Match      1  23  22,805.00
## 127 227     3 20 15.000000  Low Match      1  26  56,118.00
## 128 229     4 20 20.000000  Low Match      1  28  30,146.00
## 129 230     4 20 20.000000  Low Match      1  42  39,123.00
## 130 231     3 20 15.000000  Low Match      1  30  46,153.00
## 131 232     1 20  5.000000  Low Match      1  31  45,300.00
## 132 233     5 20 25.000000  Low Match      1  29  42,397.00
## 133 234     0  9  0.000000  Low Match      0  22  44,346.00
## 134 236     0  9  0.000000  Low Match      0  24  42,225.00
## 135 237     1  9 11.111111  Low Match      0  34  37,405.00
## 136 238     2  9 22.222222  Low Match      0  27  28,524.00
## 137 240     1  9 11.111111  Low Match      0  26  61,141.00
## 138 241     3  9 33.333333  Low Match      0  29   8,607.00
## 139 246     1  9 11.111111  Low Match      1  25  41,476.00
## 140 248     3  9 33.333333  Low Match      1  27  49,841.00
## 141 254     0 21  0.000000  Low Match      0  22  37,240.00
## 142 255     0 21  0.000000  Low Match      0  26  36,594.00
## 143 256     3 21 14.285714  Low Match      0  24  62,997.00
## 144 257     0 21  0.000000  Low Match      0  24  46,608.00
## 145 258     7 21 33.333333  Low Match      0  25  37,881.00
## 146 259     1 21  4.761905  Low Match      0  25  48,944.00
## 147 261     5 21 23.809524  Low Match      0  26  77,112.00
## 148 263     6 21 28.571429  Low Match      0  26  18,283.00
## 149 264     4 21 19.047619  Low Match      0  26  31,432.00
## 150 265     1 21  4.761905  Low Match      0  32  73,073.00
## 151 268    10 21 47.619048  Low Match      0  24  26,706.00
## 152 271     2 21  9.523810  Low Match      0  23  50,060.00
## 153 272     0 21  0.000000  Low Match      0  25  25,401.00
## 154 276     5 21 23.809524  Low Match      1  26  80,608.00
## 155 279     5 21 23.809524  Low Match      1  25  43,844.00
## 156 288     4 21 19.047619  Low Match      1  27  53,196.00
## 157 289     5 21 23.809524  Low Match      1  28  53,229.00
## 158 292     4 21 19.047619  Low Match      1  28  25,786.00
## 159 295     0 14  0.000000  Low Match      0  30  39,394.00
## 160 296     3 14 21.428571  Low Match      0  28  40,695.00
## 161 297     4 14 28.571429  Low Match      0  21  45,788.00
## 162 298     0 14  0.000000  Low Match      0  22  37,315.00
## 163 299     1 14  7.142857  Low Match      0  24  51,663.00
## 164 300     2 14 14.285714  Low Match      0  35  32,563.00
## 165 302     0 14  0.000000  Low Match      0  21  54,303.00
## 166 303     1 14  7.142857  Low Match      0  33  16,908.00
## 167 304     1 14  7.142857  Low Match      0  28  39,729.00
## 168 307     3 14 21.428571  Low Match      0  27  57,316.00
## 169 309     2 14 14.285714  Low Match      1  32  30,587.00
## 170 310     1 14  7.142857  Low Match      1  25  55,080.00
## 171 311     1 14  7.142857  Low Match      1  29  57,513.00
## 172 312     1 14  7.142857  Low Match      1  23  33,772.00
## 173 316     6 14 42.857143  Low Match      1  25  31,857.00
## 174 320     0 14  0.000000  Low Match      1  36  23,207.00
## 175 343     2 18 11.111111  Low Match      0  32  25,831.00
## 176 344     4 18 22.222222  Low Match      0  26  28,759.00
## 177 345     4 18 22.222222  Low Match      0  24  25,401.00
## 178 347     0 18  0.000000  Low Match      0  26  19,264.00
## 179 348     1 18  5.555556  Low Match      0  21  41,778.00
## 180 349     8 18 44.444444  Low Match      0  33  35,963.00
## 181 354     8 18 44.444444  Low Match      0  22  49,409.00
## 182 356     1 18  5.555556  Low Match      0  27  31,516.00
## 183 357     3 18 16.666667  Low Match      0  27  36,223.00
## 184 358     2 18 11.111111  Low Match      0  26  43,367.00
## 185 359     3 18 16.666667  Low Match      0  26  27,503.00
## 186 361     1 20  5.000000  Low Match      1  34  35,187.00
## 187 363     2 20 10.000000  Low Match      1  23  26,298.00
## 188 369     8 20 40.000000  Low Match      1  25  31,148.00
## 189 370     1 20  5.000000  Low Match      1  28  55,704.00
## 190 371     4 20 20.000000  Low Match      1  27  69,487.00
## 191 372     3 20 15.000000  Low Match      1  27  46,138.00
## 192 376     2 20 10.000000  Low Match      1  25  66,827.00
## 193 378     1 20  5.000000  Low Match      1  29  42,897.00
## 194 379     2 19 10.526316  Low Match      0  38  31,809.00
## 195 382     4 19 21.052632  Low Match      0  28  52,010.00
## 196 385     7 19 36.842105  Low Match      0  25  78,704.00
## 197 386     2 19 10.526316  Low Match      0  26  75,347.00
## 198 388     1 19  5.263158  Low Match      0  24  47,005.00
## 199 389     8 19 42.105263  Low Match      0  21  52,805.00
## 200 391     1 19  5.263158  Low Match      0  28  50,725.00
## 201 392     2 19 10.526316  Low Match      0  26  49,409.00
## 202 393     5 19 26.315789  Low Match      0  22  65,693.00
## 203 394     1 19  5.263158  Low Match      0  24  45,736.00
## 204 395     2 19 10.526316  Low Match      0  33  33,906.00
## 205 396     2 19 10.526316  Low Match      0  25  50,501.00
## 206 399     1 18  5.555556  Low Match      1  29  48,785.00
## 207 405     0 18  0.000000  Low Match      1  30  55,080.00
## 208 410     3 18 16.666667  Low Match      1  27  55,080.00
## 209 411     1 18  5.555556  Low Match      1  34  80,006.00
## 210 414     8 18 44.444444  Low Match      1  29  55,080.00
## 211 415     1 18  5.555556  Low Match      1  31  52,318.00
## 212 416     6  8 75.000000 High Match      0  25  62,844.00
## 213 418     0  8  0.000000  Low Match      0  25  52,586.00
## 214 419     1  8 12.500000  Low Match      0  28  29,236.00
## 215 420     2  8 25.000000  Low Match      0  22  31,486.00
## 216 421     2  8 25.000000  Low Match      0  25  78,704.00
## 217 422     4  6 66.666667 High Match      1  26  31,632.00
## 218 425     0  6  0.000000  Low Match      1  28 106,663.00
## 219 427     0  6  0.000000  Low Match      1  28  84,043.00
## 220 428     2  6 33.333333  Low Match      1  27  35,224.00
## 221 429     1  6 16.666667  Low Match      1  27  36,381.00
## 222 430     0 14  0.000000  Low Match      0  22  65,498.00
## 223 432     6 14 42.857143  Low Match      0  28  60,000.00
## 224 433     2 14 14.285714  Low Match      0  30  22,669.00
## 225 434     1 14  7.142857  Low Match      0  27  81,266.00
## 226 437     3 14 21.428571  Low Match      0  23  29,746.00
## 227 438     2 14 14.285714  Low Match      0  23  47,556.00
## 228 439     3 14 21.428571  Low Match      0  29  42,651.00
## 229 443     0 10  0.000000  Low Match      1  27  27,794.00
## 230 444     0 10  0.000000  Low Match      1  26  41,737.00
## 231 445     2 10 20.000000  Low Match      1  30  90,225.00
## 232 448     5 10 50.000000 High Match      1  22  55,080.00
## 233 449     2 10 20.000000  Low Match      1  23  52,280.00
## 234 454     0  6  0.000000  Low Match      0  27  56,056.00
## 235 457     0  6  0.000000  Low Match      0  55  60,835.00
## 236 459     0  6  0.000000  Low Match      0  26  62,829.00
## 237 460     1  6 16.666667  Low Match      1  33  30,038.00
## 238 465     0  6  0.000000  Low Match      1  23  16,767.00
## 239 466     0 15  0.000000  Low Match      0  24  42,967.00
## 240 467     7 15 46.666667  Low Match      0  24  21,488.00
## 241 468     3 15 20.000000  Low Match      0  25  89,977.00
## 242 470     5 15 33.333333  Low Match      0  29  18,619.00
## 243 474     4 15 26.666667  Low Match      0  30  22,161.00
## 244 475     2 15 13.333333  Low Match      0  27  82,734.00
## 245 476     2 15 13.333333  Low Match      0  25  40,163.00
## 246 479     0 15  0.000000  Low Match      0  27  46,185.00
## 247 481     1 15  6.666667  Low Match      1  23  78,844.00
## 248 482     4 15 26.666667  Low Match      1  30  29,575.00
## 249 484     2 15 13.333333  Low Match      1  31  34,752.00
## 250 492     7 15 46.666667  Low Match      1  30  22,173.00
## 251 494     2 15 13.333333  Low Match      1  30  37,994.00
## 252 495     2 15 13.333333  Low Match      1  32  35,409.00
## 253 496     1  7 14.285714  Low Match      0  23  23,707.00
## 254 498     0  7  0.000000  Low Match      0  25  33,772.00
## 255 499     2  7 28.571429  Low Match      0  26  57,501.00
## 256 500     1  7 14.285714  Low Match      0  25  25,314.00
## 257 501     1  7 14.285714  Low Match      0  26  48,876.00
## 258 503     0  6  0.000000  Low Match      1  25  34,870.00
## 259 504     2  6 33.333333  Low Match      1  23  35,848.00
## 260 506     0  6  0.000000  Low Match      1  24  45,017.00
## 261 507     1  6 16.666667  Low Match      1  24  12,416.00
## 262 508     1  6 16.666667  Low Match      1  36  87,789.00
## 263 513     6 22 27.272727  Low Match      0  23  50,572.00
## 264 514     0 22  0.000000  Low Match      0  23  49,642.00
## 265 515     2 22  9.090909  Low Match      0  23  20,000.00
## 266 516     6 22 27.272727  Low Match      0  27  32,508.00
## 267 518     6 22 27.272727  Low Match      0  23  35,627.00
## 268 520     0 22  0.000000  Low Match      0  28  46,280.00
## 269 521     7 22 31.818182  Low Match      0  26  41,191.00
## 270 522     5 22 22.727273  Low Match      0  22  71,787.00
## 271 524    14 22 63.636364 High Match      0  25  72,412.00
## 272 525     0 22  0.000000  Low Match      0  24  36,510.00
## 273 527     0 22  0.000000  Low Match      0  24  32,386.00
## 274 532     3 22 13.636364  Low Match      1  26  46,272.00
## 275 535     8 22 36.363636  Low Match      1  23  48,137.00
## 276 537     2 22  9.090909  Low Match      1  22  61,686.00
## 277 543     0 22  0.000000  Low Match      1  24  47,624.00
## 278 544     3 22 13.636364  Low Match      1  23  36,673.00
## 279 545     2 22  9.090909  Low Match      1  24  16,767.00
## 280 551     2 22  9.090909  Low Match      1  27  55,138.00
##                                                        field attr1_1
## 1                                                        Law   15.00
## 2                                                        law   45.00
## 3                                                        Law   20.00
## 4                                                        Law   20.00
## 5                                                        law   10.00
## 6                                                        Law   15.00
## 7                           Masters of Social Work&Education   15.00
## 8                                                        Law   60.00
## 9                                                        Law   30.00
## 10                                                       Law   30.00
## 11                                         political science   35.00
## 12                                                  Business   33.33
## 13                                            TC (Health Ed)   20.00
## 14                                               social work   10.00
## 15                                               Social Work   20.00
## 16                                               Social Work   20.00
## 17                                 Speech Language Pathology   17.00
## 18                                               Social Work   20.00
## 19                                                       law   35.00
## 20                                    Educational Psychology   30.00
## 21                                               Mathematics   20.00
## 22                                 Organizational Psychology   10.00
## 23                                                       Law   50.00
## 24                                                       Law   20.00
## 25                                       Operations Research   25.00
## 26                                    Mechanical Engineering   20.00
## 27                                      Mathematical Finance   20.00
## 28                                                       Law   35.00
## 29                                                       Law   25.00
## 30                                                       Law   15.00
## 31                                                       MBA   40.00
## 32                                                       LAW   20.00
## 33                                               social work   17.00
## 34                                         German Literature   15.00
## 35                                                       law   30.00
## 36                                                       Law    5.00
## 37                                               Social Work   10.00
## 38                                                       Law   20.00
## 39                                      MFA Creative Writing   21.00
## 40                                                       MBA   25.00
## 41                                                       Law   60.00
## 42                                                       law   20.00
## 43                                                  Classics   10.00
## 44                                               Social Work   15.00
## 45                                               Social Work   10.00
## 46                                                Journalism   10.00
## 47                       Elementary/Childhood Education (MA)   20.00
## 48                                    Masters of Social Work   20.00
## 49                                            Communications   15.00
## 50                                               Social Work   15.00
## 51                                                       Law   15.00
## 52                     International Educational Development   10.00
## 53                                                  Business   18.00
## 54                        Climate-Earth and Environ. Science   50.00
## 55                                               Social Work   40.00
## 56                                                       Law   30.00
## 57                                             Business- MBA   40.00
## 58                                                 chemistry   20.00
## 59                                                       law   30.00
## 60                                                       Law   10.00
## 61                                                  Religion   19.00
## 62                                                      Film   20.00
## 63                                                 Economics   15.00
## 64                                                psychology   20.00
## 65                                      Economics, Sociology   20.00
## 66                                                   English   10.00
## 67                                    psychology and english   35.00
## 68                                                       Law   40.00
## 69                                    Biomedical Engineering   50.00
## 70                           Economics and Political Science   40.00
## 71                                      Art History/medicine   20.00
## 72                                                philosophy   25.00
## 73                                          Computer Science   25.00
## 74                                                   Finance   16.67
## 75                                         marine geophysics    6.67
## 76                                                  Business   17.39
## 77                                                       MBA   20.00
## 78                                         political science   14.29
## 79                                        Nutrition/Genetics   16.00
## 80                                              Neuroscience   11.36
## 81                                    Comparative Literature   18.18
## 82                                                  Business   18.00
## 83                                   International Relations    8.51
## 84                                       History of Religion   12.00
## 85                                                       MBA   15.38
## 86                                                  business   15.09
## 87                                                  Business   14.00
## 88                                       Business, marketing   14.29
## 89                                    physics (astrophysics)   19.05
## 90                            Business/ Finance/ Real Estate   16.00
## 91                                              Biochemistry   16.67
## 92                                             Art Education   12.00
## 93                                                Philosophy   20.51
## 94                                American Studies (Masters)   16.67
## 95                                                  Business   27.78
## 96                                                   biology   18.60
## 97                                              Cell Biology   11.11
## 98                                              Microbiology   17.78
## 99                             International Affairs/Finance    9.52
## 100                                    International Affairs   12.24
## 101              international affairs/international finance   15.09
## 102                                     English and Comp Lit   19.15
## 103           Sociomedical Sciences- School of Public Health   15.38
## 104                                   International Business   16.67
## 105                                      Medical Informatics   19.44
## 106                                   electrical engineering   15.56
## 107                                             Business/Law   19.57
## 108                                                      law   20.93
## 109                                      Clinical Psychology   16.00
## 110                                             microbiology   15.38
## 111                  International Affairs and Public Health   14.00
## 112                                                  History   17.50
## 113 Business and International Affairs (MBA/MIA Dual Degree)   13.21
## 114                                              Mathematics   15.38
## 115                                              Mathematics   16.00
## 116                                                 business   19.61
## 117                                         MA Biotechnology   11.54
## 118                           International Affairs/Business   13.04
## 119                                                  Ecology   20.00
## 120                          Master in Public Administration   14.29
## 121                              Computational Biochemsistry   17.02
## 122                                                      MBA   14.89
## 123                                                      MBA    7.50
## 124                                             Neurobiology   21.28
## 125                                     History (GSAS - PhD)   17.39
## 126                                   International Business   25.64
## 127                                                  Finance   20.00
## 128                                              biomedicine   21.43
## 129                                    International Affairs   23.81
## 130                                    International Affairs   18.60
## 131                                    International Affairs   17.78
## 132                          Master of International Affairs   14.00
## 133                                  Sociology and Education   20.00
## 134                                                  Biology   15.00
## 135                                         American Studies   10.00
## 136                                      Arts Administration   15.00
## 137                                                      MBA   10.00
## 138                                                 Business   90.00
## 139                                            biotechnology   35.00
## 140                                                 Business   25.00
## 141                                   Philosophy and Physics   25.00
## 142                                                nutrition   16.67
## 143                                                 Medicine   15.00
## 144                                            Art Education   22.00
## 145                                     MA Science Education    7.00
## 146                                                 Genetics   15.00
## 147                                                  biology   15.00
## 148                                               Psychology   20.00
## 149                                                  English   15.00
## 150                  Law and English Literature (J.D./Ph.D.)   15.00
## 151                                       GS Postbacc PreMed   30.00
## 152                                              Social Work   10.00
## 153                                              Social Work   25.00
## 154                                        Molecular Biology   24.00
## 155                                                  Biology   27.00
## 156                                                Sociology   20.00
## 157             MBA / Master of International Affairs (SIPA)   20.00
## 158                                    International Affairs   20.00
## 159                               MA Teaching Social Studies   10.00
## 160                                         Education Policy   15.00
## 161                           Education- Literacy Specialist   25.00
## 162                                                  English   10.00
## 163                                              Social Work   10.00
## 164                                   Anthropology/Education   15.00
## 165                                         speech pathology   50.00
## 166                                                Education   20.00
## 167                                         Speech Pathology    5.00
## 168                                     Elementary Education   10.00
## 169                                                education   20.00
## 170                                         Computer Science   24.00
## 171                                        Finance/Economics   50.00
## 172                                      Museum Anthropology   25.00
## 173                                                 Business   25.00
## 174                                                  History   20.00
## 175                                             Epidemiology    5.00
## 176                     International Security Policy - SIPA   15.00
## 177                                                Nutrition   10.00
## 178                                                EDUCATION   30.00
## 179                                                EDUCATION   15.00
## 180                                Organizational Psychology   25.00
## 181                                      Clinical Psychology   20.00
## 182                                    Counseling Psychology   15.00
## 183                                                education   15.00
## 184                                                      MBA   20.00
## 185                              Communications in Education    9.00
## 186                                Intellectual Property Law   30.00
## 187                                              engineering   25.00
## 188                                      Clinical Psychology   17.00
## 189                                                      MBA   50.00
## 190                                                      MBA   40.00
## 191                                                 Business   30.00
## 192                                                 business   30.00
## 193                                                      MBA   25.00
## 194                                          Business, Media   15.00
## 195                                                     Film   25.00
## 196                                     Elementary Education   25.00
## 197                                        School Psychology   15.00
## 198                                    Counseling Psychology   22.00
## 199                                                      Law   20.00
## 200                                         Creative Writing   35.00
## 201                            Creative Writing - Nonfiction   15.00
## 202                             Writing: Literary Nonfiction    8.00
## 203                            Creative Writing (Nonfiction)   18.00
## 204                                       NonFiction Writing   20.00
## 205                           Theatre Management & Producing   15.00
## 206                                                 Business   25.00
## 207                                    International Affairs   30.00
## 208                                                 Business   20.00
## 209                                                  Finance   35.00
## 210                                                 business   95.00
## 211                                                      MBA   10.00
## 212                                               Psychology   15.00
## 213                                                      Law   15.00
## 214                                              Social Work    5.00
## 215                                              Social Work   10.00
## 216                                              Social Work   20.00
## 217                                   Fundraising Management   30.00
## 218                           Business (Finance & Marketing)   16.00
## 219                                    International Affairs   20.00
## 220                                                      MBA   60.00
## 221                                                 Business   55.00
## 222                        Elementary Education - Preservice   10.00
## 223                                              Social Work   15.00
## 224                                              MFA Writing   20.00
## 225                                    International Affairs   30.00
## 226                                              Social Work   15.00
## 227                                              Social Work   14.00
## 228                                      Bilingual Education   20.00
## 229                  International Affairs - Economic Policy   30.00
## 230                                                      MBA   50.00
## 231                                            SIPA - Energy   40.00
## 232                                             Law/Business   25.00
## 233                                                      Law   55.00
## 234                                              MFA  Poetry   10.00
## 235                                           SOA -- writing   25.00
## 236                                                  Finance   30.00
## 237                                                  History   20.00
## 238                                              art history   20.00
## 239                                                  working   10.00
## 240                                               Consulting   20.00
## 241                                Human Rights: Middle East   15.00
## 242                                                 medicine   10.00
## 243                                            biotechnology   15.00
## 244                               SIPA-International Affairs   10.00
## 245                               SIPA-International Affairs   20.00
## 246                                    International affairs   15.00
## 247                                      teaching of English   25.00
## 248                                                      MBA   20.00
## 249                                                     GSAS   16.00
## 250                                                  physics   30.00
## 251                                               journalism   25.00
## 252                                                  Theater   20.00
## 253                                              Engineering   20.00
## 254                                    biochemistry/genetics   18.00
## 255                                                  Biology   19.00
## 256                                             Epidemiology   10.00
## 257                                             epidemiology   30.00
## 258                                                  Biology   30.00
## 259                                         Computer Science   30.00
## 260                                                    Stats   15.00
## 261                                               Statistics   20.00
## 262                                          math of finance   18.00
## 263                                       MFA Acting Program   20.00
## 264                                                sociology   20.00
## 265                                       Nonfiction writing   20.00
## 266                      Biochemistry & Molecular Biophysics   58.00
## 267                                                  Theater   20.00
## 268                                         Social Work/SIPA   25.00
## 269                                              Social Work   10.00
## 270                                                      Law   10.00
## 271                                                 medicine   15.00
## 272                                            Public Health   10.00
## 273                                              Social Work   50.00
## 274                                   Electrical Engineering   25.00
## 275                                             Biochemistry   23.00
## 276                                   Electrical Engineering   20.00
## 277                                   Electrical Engineering   20.00
## 278                                   Biomedical Engineering   15.00
## 279                                   Biomedical Informatics   20.00
## 280                                                 Business   40.00
##     sinc1_1 intel1_1 fun1_1 amb1_1 shar1_1
## 1     20.00    20.00  15.00  15.00   15.00
## 2      5.00    25.00  20.00   0.00    5.00
## 3     20.00    20.00  20.00  10.00   10.00
## 4      5.00    25.00  25.00  10.00   15.00
## 5     25.00    20.00  25.00   5.00   15.00
## 6     15.00    25.00  20.00  15.00   10.00
## 7     15.00    15.00  40.00  10.00    5.00
## 8      0.00     0.00  40.00   0.00    0.00
## 9      5.00    15.00  40.00   5.00    5.00
## 10    10.00    20.00  10.00  10.00   20.00
## 11    15.00    25.00  10.00   5.00   10.00
## 12    11.11    11.11  11.11  11.11   22.22
## 13    20.00    20.00  10.00  10.00   20.00
## 14    20.00    20.00  15.00  20.00   15.00
## 15    20.00    15.00  15.00  15.00   15.00
## 16    20.00    20.00  15.00  15.00   10.00
## 17    10.00    18.00  20.00  25.00   10.00
## 18    22.00    18.00  15.00  15.00   10.00
## 19    10.00    20.00  20.00  10.00    5.00
## 20    20.00    30.00  10.00   0.00   10.00
## 21    23.00    23.00  22.00   7.00    5.00
## 22    10.00    30.00  25.00  10.00   15.00
## 23    10.00    20.00  10.00  10.00    0.00
## 24    20.00    20.00  15.00  15.00   10.00
## 25    20.00    25.00  20.00  10.00    0.00
## 26    25.00    20.00  15.00  10.00   10.00
## 27    20.00    20.00  20.00   5.00   15.00
## 28    10.00    20.00  15.00  10.00   10.00
## 29    10.00    35.00  15.00   0.00   15.00
## 30    20.00    15.00  30.00   5.00   15.00
## 31    20.00    20.00  20.00   0.00    0.00
## 32    25.00    25.00  10.00  10.00   10.00
## 33    18.00    18.00  15.00  17.00   15.00
## 34    15.00    20.00  15.00  15.00   20.00
## 35    10.00    20.00  20.00  10.00   10.00
## 36    15.00    45.00  25.00   0.00   10.00
## 37    25.00    25.00  10.00  10.00   20.00
## 38    15.00    20.00  20.00   5.00   10.00
## 39    17.00    22.00  20.00   8.00   13.00
## 40    10.00    20.00  20.00  15.00   10.00
## 41    15.00     0.00  15.00   0.00   10.00
## 42    20.00    20.00  20.00  10.00   10.00
## 43    10.00    30.00  20.00  10.00   20.00
## 44    20.00    20.00  20.00  15.00   10.00
## 45    25.00    20.00  10.00  20.00   15.00
## 46    10.00    20.00  20.00  20.00   20.00
## 47    20.00    20.00  20.00  10.00   10.00
## 48    25.00    15.00  15.00  15.00   10.00
## 49    20.00    20.00  20.00   5.00   20.00
## 50    15.00    20.00  15.00  20.00   15.00
## 51     3.00    50.00  20.00   2.00   10.00
## 52    20.00    20.00  20.00  20.00   10.00
## 53    18.00    18.00  19.00  13.00   14.00
## 54     7.00    20.00  23.00   0.00    0.00
## 55    20.00    15.00  20.00   0.00    5.00
## 56    30.00    20.00  10.00  10.00    0.00
## 57     5.00    20.00  10.00  10.00   15.00
## 58    15.00    25.00  15.00  20.00    5.00
## 59    15.00    20.00  20.00   0.00    5.00
## 60    20.00    35.00  10.00   5.00   20.00
## 61    24.00    21.00  23.00   5.00    8.00
## 62    20.00    15.00  15.00   5.00   25.00
## 63    15.00    25.00  25.00  15.00    5.00
## 64    10.00    30.00  20.00  15.00    5.00
## 65    10.00    20.00  20.00  20.00   10.00
## 66    10.00    35.00  35.00   8.00    2.00
## 67    15.00     5.00  30.00  15.00    0.00
## 68    15.00    10.00  20.00   5.00   10.00
## 69    10.00    20.00   5.00   5.00   10.00
## 70    10.00    10.00  10.00  10.00   20.00
## 71    15.00    20.00  15.00  12.00   18.00
## 72    40.00    15.00  10.00   5.00    5.00
## 73    10.00    20.00  20.00  10.00   15.00
## 74    16.67    16.67  16.67  16.67   16.67
## 75    20.00    20.00  17.78  20.00   15.56
## 76    17.39    15.22  17.39  13.04   19.57
## 77    20.00    20.00  20.00   6.67   13.33
## 78    14.29    21.43  21.43  19.05    9.52
## 79    16.00    16.00  18.00  18.00   16.00
## 80    18.18    22.73  18.18  11.36   18.18
## 81    18.18    18.18  18.18  13.64   13.64
## 82    18.00    20.00  18.00  16.00   10.00
## 83    19.15    19.15  19.15  19.15   14.89
## 84    20.00    20.00  20.00  14.00   14.00
## 85    17.31    17.31  17.31  17.31   15.38
## 86    18.87    18.87  16.98  16.98   13.21
## 87    18.00    16.00  18.00  18.00   16.00
## 88    16.33    16.33  18.37  18.37   16.33
## 89    19.05    19.05  19.05   9.52   14.29
## 90    18.00    18.00  18.00  14.00   16.00
## 91    18.75    18.75  18.75  18.75    8.33
## 92    20.00    20.00  20.00  16.00   12.00
## 93    23.08    20.51  17.95   2.56   15.38
## 94    12.50    20.83  18.75  12.50   18.75
## 95    19.44    19.44  27.78   2.78    2.78
## 96    13.95    18.60  16.28  16.28   16.28
## 97    17.78    17.78  17.78  17.78   17.78
## 98    20.00    17.78  15.56  15.56   13.33
## 99    23.81    16.67  16.67  16.67   16.67
## 100   20.41    16.33  14.29  20.41   16.33
## 101   16.98    16.98  16.98  18.87   15.09
## 102   21.28    17.02  17.02  12.77   12.77
## 103   19.23    19.23  17.31  17.31   11.54
## 104   16.67    16.67  16.67  16.67   16.67
## 105   19.44    22.22  16.67  11.11   11.11
## 106   11.11    22.22  15.56  20.00   15.56
## 107   19.57    15.22  17.39  15.22   13.04
## 108   16.28    23.26  16.28  11.63   11.63
## 109   16.00    20.00  16.00  18.00   14.00
## 110   19.23    19.23  19.23  13.46   13.46
## 111   18.00    20.00  18.00  16.00   14.00
## 112   17.50    20.00  12.50  20.00   12.50
## 113   18.87    18.87  16.98  16.98   15.09
## 114   19.23    19.23  19.23  15.38   11.54
## 115   20.00    18.00  20.00  18.00    8.00
## 116   15.69    17.65  15.69  17.65   13.73
## 117   13.46    19.23  19.23  19.23   17.31
## 118   21.74    17.39  17.39  15.22   15.22
## 119   15.56    20.00  15.56  11.11   17.78
## 120   18.37    18.37  16.33  18.37   14.29
## 121   21.28    17.02  21.28  14.89    8.51
## 122   19.15    21.28  19.15  14.89   10.64
## 123   17.50    20.00  22.50  12.50   20.00
## 124   17.02    21.28  12.77   6.38   21.28
## 125   17.39    19.57  19.57  10.87   15.22
## 126   17.95    15.38  20.51  10.26   10.26
## 127   16.00    18.00  16.00  16.00   14.00
## 128   16.67    21.43  16.67  11.90   11.90
## 129   23.81    23.81  23.81   2.38    2.38
## 130   20.93    23.26  23.26   2.33   11.63
## 131   17.78    17.78  17.78  13.33   15.56
## 132   20.00    18.00  16.00  14.00   18.00
## 133   20.00    20.00  10.00  10.00   20.00
## 134   20.00    15.00  20.00  15.00   15.00
## 135   20.00    20.00  20.00  20.00   10.00
## 136   20.00    20.00  15.00  15.00   15.00
## 137   30.00    30.00  10.00  10.00   10.00
## 138    2.00     2.00   2.00   2.00    2.00
## 139   20.00    10.00  20.00  10.00    5.00
## 140   15.00    20.00  20.00  15.00    5.00
## 141    7.00    25.00  25.00   8.00   10.00
## 142   16.67    16.67  16.67  16.67   16.67
## 143   25.00    20.00  20.00  10.00   10.00
## 144   14.00    30.00  10.00  14.00   10.00
## 145   35.00    20.00  20.00   5.00   13.00
## 146   18.00    19.00  19.00  17.00   12.00
## 147   20.00    20.00  20.00  15.00   10.00
## 148   20.00    20.00  10.00  20.00   10.00
## 149   15.00    25.00  20.00   5.00   20.00
## 150    5.00    45.00  10.00  15.00   10.00
## 151   20.00    20.00  10.00  15.00    5.00
## 152   15.00    40.00  15.00  10.00   10.00
## 153   20.00    25.00  25.00   5.00    0.00
## 154   16.00    28.00  14.00   8.00   10.00
## 155   15.00    28.00  10.00  10.00   10.00
## 156   20.00    15.00  25.00  10.00   10.00
## 157   30.00    10.00  20.00   0.00   20.00
## 158   18.00    20.00  17.00  10.00   15.00
## 159   18.00    20.00  16.00  16.00   20.00
## 160   20.00    35.00  15.00   5.00   10.00
## 161   25.00    15.00  15.00  10.00   10.00
## 162   30.00    20.00  10.00  15.00   15.00
## 163   40.00    10.00  20.00  10.00   10.00
## 164   20.00    25.00  15.00  15.00   10.00
## 165    5.00    20.00  10.00   5.00   10.00
## 166   25.00    25.00  10.00  15.00    5.00
## 167   20.00    25.00  25.00  15.00   10.00
## 168   20.00    20.00  15.00  20.00   15.00
## 169   20.00    20.00  20.00  10.00   10.00
## 170   15.00    20.00  20.00   8.00   13.00
## 171   20.00    10.00  10.00   5.00    5.00
## 172    5.00    30.00  15.00   5.00   20.00
## 173   20.00    15.00  25.00  10.00    5.00
## 174   16.00    16.00  16.00  16.00   16.00
## 175   25.00    20.00  10.00  15.00   25.00
## 176   15.00    15.00  20.00  20.00   15.00
## 177   20.00    27.00  23.00  15.00    5.00
## 178    5.00    10.00  20.00  30.00    5.00
## 179   25.00    15.00  15.00  15.00   15.00
## 180   20.00    10.00  20.00  20.00    5.00
## 181   10.00    20.00  20.00  20.00   10.00
## 182   25.00    20.00  20.00   0.00   20.00
## 183   20.00    20.00  20.00  20.00    5.00
## 184   10.00    25.00  25.00  10.00   10.00
## 185   17.00    23.00  15.00  14.00   22.00
## 186   20.00    30.00   5.00   5.00   10.00
## 187   20.00    20.00  20.00   5.00   10.00
## 188   18.00    28.00  27.00   5.00    5.00
## 189   10.00    10.00  10.00  10.00   10.00
## 190    0.00    10.00  50.00   0.00    0.00
## 191   15.00    15.00  20.00   5.00   15.00
## 192   10.00    15.00  20.00  15.00   10.00
## 193   15.00    20.00  20.00  10.00   10.00
## 194   15.00    20.00  15.00  15.00   20.00
## 195   25.00    25.00  10.00  10.00    5.00
## 196   20.00    20.00  20.00  10.00    5.00
## 197   10.00    30.00  10.00  15.00   20.00
## 198   17.00    15.00  15.00  15.00   16.00
## 199   30.00    20.00  20.00   0.00   10.00
## 200    0.00    35.00  10.00  10.00   10.00
## 201   20.00    15.00  30.00  10.00   10.00
## 202   20.00    25.00  25.00  12.00   10.00
## 203   19.00    16.00  15.00  15.00   17.00
## 204   20.00    20.00  10.00  10.00   20.00
## 205   18.00    18.00  18.00  15.00   16.00
## 206   10.00    20.00  20.00  10.00   15.00
## 207   25.00    20.00  15.00   5.00    5.00
## 208   20.00    20.00  20.00  10.00   10.00
## 209   15.00    15.00  25.00  10.00    0.00
## 210    1.00     1.00   1.00   1.00    1.00
## 211   20.00    40.00  10.00  10.00   10.00
## 212   20.00    30.00  20.00   5.00   10.00
## 213   25.00    20.00  15.00  10.00   15.00
## 214   30.00    20.00  10.00   5.00   30.00
## 215   10.00    40.00  10.00  15.00   15.00
## 216   10.00    30.00  20.00  10.00   10.00
## 217   25.00    20.00  10.00  10.00    5.00
## 218   25.00    20.00  12.00  12.00   15.00
## 219   20.00    20.00  20.00  20.00   20.00
## 220   10.00    10.00  10.00  10.00    0.00
## 221   10.00    15.00  15.00   5.00    0.00
## 222   25.00    15.00  20.00  15.00   15.00
## 223   30.00    30.00  10.00  10.00    5.00
## 224   20.00    20.00  20.00  10.00   10.00
## 225   15.00    15.00  30.00   0.00   10.00
## 226   15.00    18.00  18.00  14.00   20.00
## 227   15.00    16.00  17.00  18.00   20.00
## 228   10.00    50.00   5.00  10.00    5.00
## 229   25.00    10.00  20.00   0.00   15.00
## 230   10.00    10.00  30.00   0.00    0.00
## 231   12.00    18.00  12.00   6.00   12.00
## 232   15.00    25.00  25.00   5.00    5.00
## 233    2.00     1.00  40.00   1.00    1.00
## 234   20.00    20.00  20.00  10.00   20.00
## 235    0.00    25.00  25.00   0.00   25.00
## 236   10.00    10.00  30.00  10.00   10.00
## 237   10.00    20.00  15.00  15.00   20.00
## 238   20.00    20.00  20.00  20.00    0.00
## 239   20.00    20.00  15.00  20.00   15.00
## 240   20.00    20.00  20.00  20.00    0.00
## 241   25.00    15.00  15.00  15.00   15.00
## 242   20.00    25.00  25.00  10.00   10.00
## 243   20.00    10.00  30.00   5.00   20.00
## 244   15.00    30.00  10.00  15.00   20.00
## 245   20.00    20.00  15.00  15.00   10.00
## 246   15.00    20.00  20.00  15.00   15.00
## 247   20.00    15.00  20.00   5.00   15.00
## 248   10.00    22.00  20.00  15.00   13.00
## 249   16.00    20.00  16.00  16.00   16.00
## 250    0.00    30.00  30.00  10.00    0.00
## 251   25.00    25.00  25.00   0.00    0.00
## 252   20.00    20.00  15.00  15.00   10.00
## 253   30.00    20.00  20.00  10.00    0.00
## 254   18.00    18.00  18.00  18.00   10.00
## 255   19.00    19.00  19.00   5.00   19.00
## 256   15.00    35.00  15.00  10.00   15.00
## 257   10.00    20.00  10.00  10.00   20.00
## 258   30.00    40.00   0.00   0.00    0.00
## 259    0.00    30.00  15.00  15.00   10.00
## 260   15.00    20.00  15.00  15.00   20.00
## 261   20.00    20.00  20.00  20.00    0.00
## 262   10.00    18.00  18.00  18.00   18.00
## 263   20.00    20.00  20.00   5.00   15.00
## 264   20.00    20.00  15.00   5.00   20.00
## 265   20.00    20.00  15.00  10.00   15.00
## 266    5.00     8.00  10.00   7.00   12.00
## 267   25.00    25.00  30.00   5.00    5.00
## 268   15.00    25.00  15.00  10.00   10.00
## 269   30.00    20.00  15.00  15.00   10.00
## 270   20.00    15.00  20.00  15.00   20.00
## 271   20.00    25.00  20.00  10.00   10.00
## 272   15.00    30.00  20.00  15.00   10.00
## 273   20.00    10.00   5.00  10.00    5.00
## 274   20.00    20.00  20.00   5.00   10.00
## 275   14.00    19.00  16.00  10.00   18.00
## 276    5.00    25.00  25.00   5.00   20.00
## 277   16.00    16.00  17.00  16.00   15.00
## 278   25.00    20.00  10.00  10.00   20.00
## 279   20.00    20.00  20.00  10.00   10.00
## 280   20.00    20.00  20.00   0.00    0.00

Separate the clean data in to males and females

Speed_dating_female= subset(Speed_dating_final, gender == "0")
Speed_dating_male= subset(Speed_dating_final, gender == "1")

Create a pie chart of percentage of match in males

ggplot(Speed_dating_male, aes(x=group))+ geom_bar()

Create a pie chart of percentage of match in females

female_percent = plot_ly(Speed_dating_female, labels = ~group, values = ~percent, type = "pie") %>% layout(title = "Female", showlegend = TRUE)
female_percent

Plot the mean score of 5 attributes by groups in males

mean_male = setNames(aggregate(Speed_dating_male[,c( "attr1_1", "sinc1_1", "intel1_1", "fun1_1", "amb1_1", "shar1_1")],by=list(Speed_dating_male$group),mean), c("Group","Attraction", "Sincerity", "Intelligence ", "Fun","Ambition","Share")) 
mean_male_long = melt(mean_male,id.vars="Group")

ggplot(mean_male_long, aes(fill=Group, y=value, x=variable)) + 
    geom_bar(position="dodge", stat="identity") + ylab("Score") + xlab("") + ggtitle("Mean Scores of Different Variables in Male") +theme(plot.title = element_text(hjust = 0.5))

Plot the mean score of 5 attributes by groups in females

mean_female = setNames(aggregate(Speed_dating_female[,c( "attr1_1", "sinc1_1", "intel1_1", "fun1_1", "amb1_1", "shar1_1")],by=list(Speed_dating_female$group),mean), c("Group","Attraction", "Sincerity", "Intelligence ", "Fun","Ambition","Share")) 
mean_female_long = melt(mean_female,id.vars="Group")

ggplot(mean_female_long, aes(fill=Group, y=value, x=variable)) + 
    geom_bar(position="dodge", stat="identity") + ylab("Score") + xlab("")+ggtitle("Mean Scores of Different Variables in Male") +theme(plot.title = element_text(hjust = 0.5))

Speed_dating = read.csv("SpeedDating.csv", na.strings = c("",".","NA"), stringsAsFactors =  FALSE)
Speed_dating[Speed_dating$gender == 0,]$gender = "W"
Speed_dating[Speed_dating$gender == 1,]$gender = "M"
Speed_dating %>% 
  group_by(iid) %>% 
  summarise(gender = head(gender,1)) %>% 
  group_by(gender) %>% 
  summarise(count = n())
## # A tibble: 2 x 2
##   gender count
##   <chr>  <int>
## 1 M        277
## 2 W        274
gender_waves = subset(Speed_dating, !duplicated(Speed_dating[, 1])) %>%
  group_by(wave, gender) %>%
  summarise(count = n()) %>%
  melt(id.vars = c("gender", "wave"))
ggplot(gender_waves, aes(x = wave, y = value, fill = factor(gender))) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_discrete(name = "Gender") +
  xlab("Wave") + ylab("Population") + ggtitle("Gender repartition in waves") 

age_waves = subset(Speed_dating, !duplicated(Speed_dating[, 1])) %>% 
  filter(!is.na(age)) %>%
  group_by(wave, gender) %>%
  summarise(count = mean(age)) %>%
  melt(id.vars = c("gender", "wave"))
ggplot(age_waves, aes(x = wave, y = value, fill = factor(gender))) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_discrete(name = "Gender") +
  xlab("Wave") + ylab("Population") + ggtitle("Age repartition in waves") 

# Group df by age and age_0
age_analysis = Speed_dating %>%
  group_by(age, age_o) %>%
  summarise(
    people = n(), 
    matches = sum(match)) %>%
  filter(!is.na(age) & !is.na(age_o))

# Filter with age difference > 5 years, and with more than 5 matches
age_diff = age_analysis %>%
  filter(age - age_o >= 0) %>%
  mutate(years = age - age_o) %>%
  group_by(years) %>%
  summarise(
    matches = sum(matches)) %>%
  arrange(years)

# Graph result
ggplot(age_diff[age_diff$years < 20,], aes(x = years, y = matches)) +
  geom_bar(stat = "identity", position = "dodge") +
  xlab("Number of years of difference between people's age") + 
  ylab("Number of matches") + ggtitle("Does age really matter?")

fields_cd = c(
  "Law",
  "Math",
  "Social Science, Psychologist" ,
  "Medical Science, Pharmaceuticals, and Bio Tech",
  "Engineering",
  "English/Creative Writing/ Journalism",
  "History/Religion/Philosophy",
  "Business/Econ/Finance",
  "Education, Academia",
  "Biological Sciences/Chemistry/Physics",
  "Social Work" ,
  "Undergrad/undecided" ,
  "Political Science/International Affairs" ,
  "Film",
  "Fine Arts/Arts Administration",
  "Languages",
  "Architecture",
  "Other"
)

# Create career codes
career_cd = c(
  "Lawyer",
  "Academic/Research", 
  "Psychologist", 
  "Doctor/Medicine",
  "Engineer", 
  "Creative Arts/Entertainment",
  "BankingBusiness/CEO/Admin",
  "Real Estate",
  "International/Humanitarian Affairs",
  "Undecided" ,
  "Social Work",
  "Speech Pathology",
  "Politics",
  "Pro sports/Athletics",
  "Other",
  "Journalism",
  "Architecture"
)

# Find number of men/women on each study field
fields = Speed_dating[!is.na(Speed_dating$field_cd),] %>%
  group_by(gender, field_cd) %>%
  summarise(
    count = n()
  )

# Find number of men/women on each career
careers = Speed_dating[!is.na(Speed_dating$career_c),] %>%
  group_by(gender, career_c) %>%
  summarise(
    count = n()
  )

# Plot study fields repartition
ggplot(fields, aes(x = field_cd, y = count, fill = factor(gender))) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_discrete(name = "Gender") +
  xlab("Field") + ylab("Count") + ggtitle("Study fields repartition") +
  scale_x_continuous(labels = fields_cd, breaks = 1:18) +
  coord_flip()

ggplot(careers, aes(x = career_c, y = count, fill = factor(gender))) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_discrete(name = "Gender") +
  xlab("Career") + ylab("Count") + ggtitle("Careers repartition") +
  scale_x_continuous(labels = career_cd, breaks = 1:17) +
  coord_flip()

race_c = c(
  "European/Caucasian-American",
  "Black/African American",
  "Asian/Pacific Islander/Asian-American",
  "Latino/Hispanic American",
  "Native American",
  "Other"
)

# Find number of men/women for each race
races = Speed_dating[!is.na(Speed_dating$race),] %>%
  group_by(gender, race) %>%
  summarise(
    my = n()
  )

# Plot race repartition
ggplot(races, aes(x = race, y = my, fill = factor(gender))) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_discrete(name = "Gender") +
  xlab("Race") + ylab("Count") + ggtitle("Race repartition") +
  scale_x_continuous(labels = race_c, breaks = 1:6) +
  coord_flip()

match_by_gender = Speed_dating %>%
  group_by(gender) %>%
  summarise(
    matches = sum(match == 1),
    fails = sum(match == 0)) %>% 
  melt(id.vars = "gender")

# Plot matches for both men and women
ggplot(match_by_gender, aes(x = variable, y = value, fill = factor(gender))) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_discrete(name = "Gender") + ggtitle("Matches by gender") +
  xlab("Result") + ylab("Count")

match_by_waves = Speed_dating[Speed_dating$match == 1,] %>%
  group_by(wave) %>%
  summarise(
    matches = sum(match == 1)
  )

# Plot matches for waves: what was the best wave to be?
ggplot(match_by_waves, aes(x = wave, y = matches)) +
  geom_bar(stat = "identity", position = "dodge") + ggtitle("Matches by waves") +
  xlab("Wave number") + ylab("Matches")

male = Speed_dating[Speed_dating$gender == "M",]
first_col = head(grep("sports", colnames(Speed_dating)),1)
last_col = head(grep("yoga", colnames(Speed_dating)),1)
match_col = head(grep("match", colnames(Speed_dating)),1)
male = male[complete.cases(male[first_col:last_col]),]

combined_male = male %>% group_by(iid) %>% summarise(match_sum = sum(match))

number_male = combined_male %>% group_by(match_sum) %>% summarise(count = n())

ggplot(number_male, aes(x = match_sum, y = count))+ geom_bar(stat = "identity", position = "dodge", colour = "black") + ggtitle("Number of men per number of matches") + xlab("Number of matches")

female = Speed_dating[Speed_dating$gender == "W",]
first_col = head(grep("sports", colnames(Speed_dating)),1)
last_col = head(grep("yoga", colnames(Speed_dating)),1)
match_col = head(grep("match", colnames(Speed_dating)),1)
female = female[complete.cases(female[first_col:last_col]),]

combined_female = female %>% group_by(iid) %>% summarise(match_sum = sum(match))

number_female = combined_female %>% group_by(match_sum) %>% summarise(count = n())

ggplot(number_female, aes(x = match_sum, y = count))+ geom_bar(stat = "identity", position = "dodge", colour = "black") + ggtitle("Number of women per number of matches") + xlab("Number of matches")

# important features for men
men_matches =  male[Speed_dating$match == 1,]
men_feature = men_matches %>%
  group_by(gender) %>%
  summarise(
    sports = sum(sports, na.rm=T),
    tvsports = sum(tvsports, na.rm=T),
    exercise = sum(exercise, na.rm=T),
    dining = sum(dining, na.rm=T),
    museums = sum(museums, na.rm=T),
    art = sum(art, na.rm=T),
    hiking = sum(hiking, na.rm=T),
    gaming = sum(gaming, na.rm=T),
    clubbing = sum(clubbing, na.rm=T),
    reading = sum(reading, na.rm=T),
    tv = sum(tv, na.rm=T),
    music = sum(music, na.rm=T),
    theater = sum(theater, na.rm=T),
    movies = sum(movies, na.rm=T),
    concerts = sum(concerts, na.rm=T),
    shopping = sum(shopping, na.rm=T),
    yoga = sum(yoga, na.rm=T)
  ) %>%
  melt(id_vars = "gender")
## Using gender as id variables
ggplot(men_feature[,c(2,3)], aes(x = reorder(variable, -value), y = value)) +
  geom_bar(stat = "identity", position = "dodge", colour="black") +
  xlab("Feature") + ylab("Count") + ggtitle("Importance of a feature for men") +
  coord_flip()

women_matches =  female[Speed_dating$match == 1,]
women_feature = women_matches %>%
  group_by(gender) %>%
  summarise(
    sports = sum(sports, na.rm=T),
    tvsports = sum(tvsports, na.rm=T),
    exercise = sum(exercise, na.rm=T),
    dining = sum(dining, na.rm=T),
    museums = sum(museums, na.rm=T),
    art = sum(art, na.rm=T),
    hiking = sum(hiking, na.rm=T),
    gaming = sum(gaming, na.rm=T),
    clubbing = sum(clubbing, na.rm=T),
    reading = sum(reading, na.rm=T),
    tv = sum(tv, na.rm=T),
    music = sum(music, na.rm=T),
    theater = sum(theater, na.rm=T),
    movies = sum(movies, na.rm=T),
    concerts = sum(concerts, na.rm=T),
    shopping = sum(shopping, na.rm=T),
    yoga = sum(yoga, na.rm=T)
  ) %>%
  melt(id_vars = "gender")
## Using gender as id variables
ggplot(women_feature[,c(2,3)], aes(x = reorder(variable, -value), y = value)) +
  geom_bar(stat = "identity", position = "dodge", colour="black") +
  xlab("Feature") + ylab("Count") + ggtitle("Importance of a feature for Women") +
  coord_flip()

Speed_dating_combined = Speed_dating[!duplicated(Speed_dating$iid), ]
Speed_dating_combined$zipcode = as.numeric(gsub(",","",Speed_dating_combined$zipcode))
Speed_dating_zip = clean.zipcodes(Speed_dating_combined$zipcode)
data(zipcode)
us = map_data('state')
plot = subset(zipcode, subset = (zip %in% Speed_dating_zip))

ggplot(plot, aes(longitude,latitude))+geom_polygon(data=us,aes(x=long,y=lat,group=group),alpha=.35) + geom_point(size = 1.3, colour="#0072B2", alpha = .25) + xlim(-125,-65)+ylim(20,50) + theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_blank(), axis.text = element_blank(), axis.title = element_blank(),axis.ticks = element_blank(),plot.title = element_text(hjust = 0.5)) + ggtitle("Location of Participants")
## Warning: Removed 5 rows containing missing values (geom_point).

invalid_zipcode = rbind(Speed_dating_combined[(is.na(Speed_dating_combined$zipcode)),], Speed_dating_combined[grep("^[0-9]{1,3}$",Speed_dating_combined$zipcode),], Speed_dating_combined[grep("^[0-9]{6,7}$",Speed_dating_combined$zipcode),])

# number of people who did not provide zipcode or from different country
count(invalid_zipcode)
## # A tibble: 1 x 1
##       n
##   <int>
## 1   128
citation(package="readxl")
## 
## To cite package 'readxl' in publications use:
## 
##   Hadley Wickham and Jennifer Bryan (2017). readxl: Read Excel
##   Files. R package version 1.0.0.
##   https://CRAN.R-project.org/package=readxl
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {readxl: Read Excel Files},
##     author = {Hadley Wickham and Jennifer Bryan},
##     year = {2017},
##     note = {R package version 1.0.0},
##     url = {https://CRAN.R-project.org/package=readxl},
##   }
citation(package="ggplot2")
## 
## To cite ggplot2 in publications, please use:
## 
##   H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
##   Springer-Verlag New York, 2009.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Book{,
##     author = {Hadley Wickham},
##     title = {ggplot2: Elegant Graphics for Data Analysis},
##     publisher = {Springer-Verlag New York},
##     year = {2009},
##     isbn = {978-0-387-98140-6},
##     url = {http://ggplot2.org},
##   }
citation(package="data.table")
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## To cite package 'data.table' in publications use:
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##   Matt Dowle and Arun Srinivasan (2017). data.table: Extension of
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##   https://CRAN.R-project.org/package=data.table
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {data.table: Extension of `data.frame`},
##     author = {Matt Dowle and Arun Srinivasan},
##     year = {2017},
##     note = {R package version 1.10.4},
##     url = {https://CRAN.R-project.org/package=data.table},
##   }
citation(package="dplyr")
## 
## To cite package 'dplyr' in publications use:
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## 
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##   }
citation(package="maps")
## 
## To cite package 'maps' in publications use:
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## 
## A BibTeX entry for LaTeX users is
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##     title = {maps: Draw Geographical Maps},
##     author = {Original S code by Richard A. Becker and Allan R. Wilks. R version by Ray Brownrigg. Enhancements by Thomas P Minka and Alex Deckmyn.},
##     year = {2017},
##     note = {R package version 3.2.0},
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##   }
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##     volume = {5},
##     number = {1},
##     pages = {144--161},
##     url = {http://journal.r-project.org/archive/2013-1/kahle-wickham.pdf},
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## To cite package 'zipcode' in publications use:
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## A BibTeX entry for LaTeX users is
## 
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##     title = {zipcode: U.S. ZIP Code database for geocoding},
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##     url = {https://CRAN.R-project.org/package=zipcode},
##   }
## 
## ATTENTION: This citation information has been auto-generated from
## the package DESCRIPTION file and may need manual editing, see
## 'help("citation")'.
citation(package="reshape2")
## 
## To cite reshape2 in publications use:
## 
##   Hadley Wickham (2007). Reshaping Data with the reshape Package.
##   Journal of Statistical Software, 21(12), 1-20. URL
##   http://www.jstatsoft.org/v21/i12/.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {Reshaping Data with the {reshape} Package},
##     author = {Hadley Wickham},
##     journal = {Journal of Statistical Software},
##     year = {2007},
##     volume = {21},
##     number = {12},
##     pages = {1--20},
##     url = {http://www.jstatsoft.org/v21/i12/},
##   }
citation(package="shiny")
## 
## To cite package 'shiny' in publications use:
## 
##   Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan
##   McPherson (2017). shiny: Web Application Framework for R. R
##   package version 1.0.5. https://CRAN.R-project.org/package=shiny
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {shiny: Web Application Framework for R},
##     author = {Winston Chang and Joe Cheng and JJ Allaire and Yihui Xie and Jonathan McPherson},
##     year = {2017},
##     note = {R package version 1.0.5},
##     url = {https://CRAN.R-project.org/package=shiny},
##   }
"Leverger, speed-dating-experiment-r (2016), GitHub repository,https://colinleverger.github.io/speed-dating-experiment-r/"
## [1] "Leverger, speed-dating-experiment-r (2016), GitHub repository,https://colinleverger.github.io/speed-dating-experiment-r/"